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Calibration and Validation of a Soil‐Landscape Model for Predicting Soil Drainage Class
Author(s) -
Bell James C.,
Cunningham Robert L.,
Havens Matthew W.
Publication year - 1992
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1992.03615995005600060035x
Subject(s) - bedrock , drainage , soil map , hydrology (agriculture) , digital elevation model , soil survey , geology , digital soil mapping , ridge , soil water , elevation (ballistics) , soil science , environmental science , sampling (signal processing) , soil series , soil classification , geomorphology , remote sensing , ecology , mathematics , geotechnical engineering , geometry , paleontology , filter (signal processing) , computer science , computer vision , biology
Abstract The soil‐landscape relationships frequently used for soil mapping activities are seldom documented. We developed a statistical model that relates soil drainage classes to eight landscape parameters describing slope morphology, proximity to surface drainage features, and soil parent material. Soil profiles and landscape parameters were described at 305 randomly selected sampling points within the Mifflintown 7.5‐min topographic quadrangle in the unglaciated ridge and valley physiographic province of central Pennsylvania. Variables defining the spatial structure of the landscape were derived from digitized 1:24 000 scale maps of streams and drainageways, surficial geology, bedrock geology, and a digital elevation model. These data were stored in a geographic information system and overlaid with the sampling point locations to define a database of 305 known soil‐landscape combinations. These soil‐landscape combinations were used to derive a statistical soil‐landscape model using multivariate discriminant analysis and class frequency information. For soil drainage class, a 74% overall agreement with field observations was found for the model using a cross‐validation approach, compared with 69% for the published soil survey. The model correctly predicted a majority of the observations within each drainage class and provided a consistent method of extrapolating point information about soils to the three‐dimensional landscape.